The integration of Artificial Intelligence (AI) into Clinical Decision Support Systems (CDSS) marks a significant evolution from rule-based alerts to predictive and prescriptive analytics, aiming to augment clinical reasoning and improve patient outcomes. This article examines the transformative potential and practical challenges of AI-driven CDSS within modern healthcare. The primary objective is to analyze how machine learning and natural language processing enhance diagnostic accuracy, personalize treatment recommendations, and predict patient risks, thereby transitioning support from information retrieval to intelligent inference. Through a review of current implementations, peer-reviewed studies, and emerging frameworks, the article evaluates the efficacy of AI-CDSS across various specialties, including radiology, oncology, and primary care. Results indicate that these systems can outperform traditional methods in specific tasks, such as detecting anomalies in medical imaging, identifying sepsis early, and optimizing complex drug regimens, leading to measurable reductions in diagnostic errors and adverse events. Furthermore, AI-CDSS holds promise for managing information overload by synthesizing vast electronic health record data into actionable insights. However, the conclusion emphasizes that realizing this promise requires meticulously addressing critical limitations. Key challenges include ensuring algorithmic transparency and mitigating bias, achieving seamless clinical workflow integration without increasing clinician burnout, establishing rigorous standards for clinical validation and regulatory approval, and resolving ethical dilemmas around accountability. Ultimately, AI-CDSS represents a powerful tool not for replacing clinicians but for creating a collaborative partnership where data-driven intelligence enhances human expertise, contingent upon building trust, demonstrating real-world utility, and fostering an ecosystem of continuous learning and oversight.

Artificial Intelligence, Clinical Decision Support Systems, Predictive Analytics, Machine Learning, Diagnostic Accuracy, Patient Safety, Clinical Workflow, Algorithmic Bias